TY - GEN
T1 - Mining multi-dimensional constrained gradients in data cubes
AU - Dong, Guozhu
AU - Han, Jiawei
AU - Lam, Joyce
AU - Pei, Jian
AU - Wang, Ke
PY - 2001
Y1 - 2001
N2 - Constrained gradient analysis (similar to the "cubegrade" problem posed by Imielinski, et al. [9]) is to extract pairs of similar cell characteristics associated with big changes in measure in a data cube. Cells are considered similar if they are related by roll-up, drill-down, or 1-dimensional mutation operation. Constrained gradient queries are expressive, capable of capturing trends in data and answering "what-if" questions. To facilitate our discussion, we call one cell in a gradient pair probe cell and the other gradient cell. An efficient algorithm is developed, which pushes constraints deep into the computation process, finding all gradient-probe cell pairs in one pass. It explores bi-directional pruning between probe cells and gradient cells, utilizing transformed measures and dimensions. Moreover, it adopts a hyper-tree structure and an H-cubing method to compress data and maximize sharing of computation. Our performance study shows that this algorithm is efficient and scalable.
AB - Constrained gradient analysis (similar to the "cubegrade" problem posed by Imielinski, et al. [9]) is to extract pairs of similar cell characteristics associated with big changes in measure in a data cube. Cells are considered similar if they are related by roll-up, drill-down, or 1-dimensional mutation operation. Constrained gradient queries are expressive, capable of capturing trends in data and answering "what-if" questions. To facilitate our discussion, we call one cell in a gradient pair probe cell and the other gradient cell. An efficient algorithm is developed, which pushes constraints deep into the computation process, finding all gradient-probe cell pairs in one pass. It explores bi-directional pruning between probe cells and gradient cells, utilizing transformed measures and dimensions. Moreover, it adopts a hyper-tree structure and an H-cubing method to compress data and maximize sharing of computation. Our performance study shows that this algorithm is efficient and scalable.
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M3 - Conference contribution
AN - SCOPUS:0012269410
T3 - VLDB 2001 - Proceedings of 27th International Conference on Very Large Data Bases
SP - 321
EP - 330
BT - VLDB 2001 - Proceedings of 27th International Conference on Very Large Data Bases
A2 - Apers, Peter M. G.
A2 - Atzeni, Paolo
A2 - Snodgrass, Richard T.
A2 - Ceri, Stefano
A2 - Ramamohanarao, Kotagiri
A2 - Paraboschi, Stefano
PB - Morgan Kaufmann
T2 - 27th International Conference on Very Large Data Bases, VLDB 2001
Y2 - 11 September 2001 through 14 September 2001
ER -